Nuclear Medicine

A methodological assessment of information complementarity between MRI and GAN-based synthetic PET for multimodal medical image analysis.

Multimodal medical imaging aims to enhance analysis by combining complementary anatomical and functional information. However, access to functional modalities such as positron emission tomography (PET) is limited in many clinical settings, prompting efforts to synthesize PET-like images from magnetic resonance imaging (MRI) using generative models. Despite growing interest, the degree to which MRI...

Waste-to-sensor upcycling of polyethylene terephthalate over Ag/Zr-MOF photocatalyst for microplastic degradation and AI-assisted heavy metal detection.

This study aims to develop an integrated waste to sensor platform that couples photocatalytic PET depolymerization with on stream quantification of coexisting heavy metals. We achieve this by preparing AIM-202 through surface coordination of Ag to a water stable Zr-aspartate MOF (MIP-202), creating Ag0/Agδ+ electron sink sites that enhance charge separation and reactive oxygen species generation w...

Brain PET in the era of anti-amyloid-β antibody therapy for Alzheimer disease.

Here the current and emerging roles of brain positron emission tomography (PET) in Alzheimer's disease (AD) in the era of anti-amyloid-β antibody ther...

Updates on tuberculosis imaging.

Tuberculosis (TB) remains a leading infectious cause of morbidity and mortality worldwide, and major diagnostic and therapeutic challenges persist des...

A Kernel Space-based Multidimensional Sparse Model for Dynamic PET Image Denoising.

Achieving high image quality for temporal frames in dynamic positron emission tomography (PET) is challenging due to the limited statistic especially ...

Graves' disease: a new era of pathophysiology-guided therapeutics.

Graves' disease (GD) is an autoimmune entity that had an unchanged treatment paradigm for more than half a century with radioactive iodine, antithyroi...

The Potency of DCLGAN for Reducing Scan time in PET/CT imaging with different radiotracers of clinical importance.

BACKGROUND AND PURPOSE: Shortening PET/CT acquisition without degrading diagnostic or quantitative performance would improve patient comfort and scann...

Interpretable predictions from whole-body FDG-PET/CT using parameters associated with clinical outcome.

BACKGROUND: Accurate prediction of clinical outcomes is challenging yet important for patient care. The aim of the study was to evaluate a deep learni...

AI-based synthetic CT attenuation correction enables reliable quantitative SPECT in unilateral condylar hyperplasia.

PURPOSE: Unilateral condylar hyperplasia (UCH) is a rare mandibular growth disorder in which accurate assessment of condylar metabolic activity is ess...

Machine learning models for predicting microvascular invasion in hepatocellular carcinoma with three-dimensional whole-lesion 18F-FDG PET radiomics.

OBJECTIVE: To estimate the performance of machine learning models based on preoperative three-dimensional whole-lesion radiomics features for predicti...

Effective elimination of respiratory misregistration-induced attenuation correction errors in PET/CT via deep learning trained on data-driven gated PET from strictly respiratory-phase-matched PET/CT cases.

[Exploration and clinical application of the "digital and intelligent surgery" diagnosis and treatment workflow for oral and maxillofacial tumors].

Tumors in the oral and maxillofacial region present significant clinical challenges due to anatomical complexity and high individual variability, with...

The SPECT/CT radiomics-based classification of skeletal metastases and benign bone lesions.

Hybrid Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) improves lesion localization and diagnostic accuracy in detecting ske...

Interpretable multimodal PET/CT-EHR fusion via mixture-of-experts for prognostic stratification in mantle cell lymphoma: a multicenter study.

BACKGROUND: Mantle cell lymphoma (MCL) is a rare, biologically heterogeneous B-cell malignancy with highly variable outcomes. Existing prognostic tool...

Non-invasive diagnosis strategy integrating PSMA PET/CT and mpMRI for patients with suspected prostate cancer: a multi-center study.

BACKGROUND: Multiparametric MRI (mpMRI) and ^68 Ga-PSMA PET/CT are widely used for prostate cancer (PCa) diagnosis but remain limited by false positiv...

Deep learning-guided discovery and engineering of binding peptides for accelerated enzymatic degradation of polyethylene terephthalate.

Enzymatic polyethylene terephthalate (PET) degradation holds promise for environmental restoration. However, limited substrate catalytic capacity hind...

Design and first experimental results of an edgeless PET prototype for breast imaging: DeepBreast.

BACKGROUND: Breast cancer causes the largest number of cancer-related deaths among women worldwide. With the aim of improving Positron Emission Tomogr...

Integrating Machine Learning Tools in Protein Design: A Case of MHETase Engineering for PET Biodeconstruction.

The integration of machine learning tools into protein engineering offers substantial promise, yet linking computational predictions to experimental p...

[177Lu]Lu-PSMA extravasation: from quantitative imaging to clinical guidelines.

Extravasation of therapeutic radioligands such as [177Lu]Lu-PSMA-617 or [177Lu]Lu-DOTATATE is rare but may result in localized radiation injury. In th...

Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative.

We present a large whole-body and total-body curated dataset of dual-modality 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-Positron Emission Tomography/Compu...